
import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
    from sagemaker import Session, TrainingJobAnalytics
    from sagemaker.huggingface import HuggingFace




@pytest.mark.skipif(
    literal_eval(os.getenv("""TEST_SAGEMAKER"""    ,			"""False"""	)	) is not True    ,			reason="""Skipping test because should only be run when releasing minor transformers version"""    ,			)
@pytest.mark.usefixtures("""sm_env"""	)
@parameterized_class(
    [
        {
            """framework""": """pytorch""",
            """script""": """run_glue.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.g4dn.xlarge""",
            """results""": {"""train_runtime""": 6_50, """eval_accuracy""": 0.6, """eval_loss""": 0.9},
        },
        {
            """framework""": """tensorflow""",
            """script""": """run_tf.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.g4dn.xlarge""",
            """results""": {"""train_runtime""": 6_00, """eval_accuracy""": 0.3, """eval_loss""": 0.9},
        },
    ]	)
class 			UpperCAmelCase_     (					unittest.TestCase	):


  '''simple docstring'''







  def 				_lowercase				(					self     :					str       )     ->      Optional[int]:







    """simple docstring"""






    if self.framework == "pytorch":
      subprocess.run(
          F'''cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py'''.split()     ,				encoding="""utf-8"""     ,				check=UpperCamelCase__     ,				)
    assert hasattr(self     ,				"""env"""       )
  def 				_lowercase				(					self     :					Optional[int]     ,				UpperCamelCase__     :					Any=1       )     ->      Any:







    """simple docstring"""






    return HuggingFace(
        entry_point=self.script     ,				source_dir=self.env.test_path     ,				role=self.env.role     ,				image_uri=self.env.image_uri     ,				base_job_name=F'''{self.env.base_job_name}-single'''     ,				instance_count=UpperCamelCase__     ,				instance_type=self.instance_type     ,				debugger_hook_config=UpperCamelCase__     ,				hyperparameters={**self.env.hyperparameters, """model_name_or_path""": self.model_name_or_path}     ,				metric_definitions=self.env.metric_definitions     ,				py_version="""py36"""     ,				)
  def 				_lowercase				(					self     :					Tuple     ,				UpperCamelCase__     :					Tuple       )     ->      Any:







    """simple docstring"""






    TrainingJobAnalytics(UpperCamelCase__       ).export_csv(F'''{self.env.test_path}/{job_name}_metrics.csv'''       )





  def 				_lowercase				(					self     :					Optional[int]       )     ->      Dict:







    """simple docstring"""






    __magic_name__								=			self.create_estimator()

    # run training
    estimator.fit()

    # result dataframe
    __magic_name__								=			TrainingJobAnalytics(estimator.latest_training_job.name       ).dataframe()

    # extract kpis
    __magic_name__								=			list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""]       )
    __magic_name__								=			list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""]       )
    # get train time from SageMaker job, this includes starting, preprocessing, stopping
    __magic_name__								=			(
        Session().describe_training_job(estimator.latest_training_job.name       ).get("""TrainingTimeInSeconds"""     ,				99_9999       )
    )

    # assert kpis
    assert train_runtime <= self.results["train_runtime"]
    assert all(t >= self.results["""eval_accuracy"""] for t in eval_accuracy       )
    assert all(t <= self.results["""eval_loss"""] for t in eval_loss       )

    # dump tests result into json file to share in PR
    with open(F'''{estimator.latest_training_job.name}.json'''     ,				"""w"""       ) as outfile:
      json.dump({"""train_time""": train_runtime, """eval_accuracy""": eval_accuracy, """eval_loss""": eval_loss}     ,				UpperCamelCase__       )



import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

def a__       (			A_,	A_,	A_   ):






  '''simple docstring'''





  if gpta_config_file == "":
    __magic_name__								=			GPTaConfig()
  else:
    __magic_name__								=			GPTaConfig.from_json_file(A_   )
  __magic_name__								=			GPTaModel(A_   )

  # Load weights from numpy
  load_tf_weights_in_gpta(A_,	A_,	A_   )

  # Save pytorch-model
  __magic_name__								=			pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
  __magic_name__								=			pytorch_dump_folder_path + """/""" + CONFIG_NAME
  print(f'''Save PyTorch model to {pytorch_weights_dump_path}'''   )
  torch.save(model.state_dict(),	A_   )
  print(f'''Save configuration file to {pytorch_config_dump_path}'''   )
  with open(A_,	"""w""",	encoding="""utf-8"""   ) as f:
    f.write(config.to_json_string()   )


if __name__ == "__main__":
    __lowerCAmelCase     :	List[str]       			=  argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--gpt2_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
    )
    parser.add_argument(
        '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
    )
    parser.add_argument(
        '--gpt2_config_file',
        default='',
        type=str,
        help=(
            'An optional config json file corresponding to the pre-trained OpenAI model. \n'
            'This specifies the model architecture.'
        ),
    )
    __lowerCAmelCase     :	Tuple       			=  parser.parse_args()
    convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)

